摘要
本文基于1999-2008年中国商业银行的相关数据,运用非参数DEA模型的Malmquist生产率指数,测算了1999-2008年中国商业银行的全要素生产率,进而将其分解为两个有意义的指数,即技术效率指数和技术进步指数,接着构建PanelData模型对影响中国商业银行全要素生产率的因素进行了实证检验。所涉及的商业银行样本覆盖了中国四大国有商业银行、股份制商业银行、城市商业银行以及外资银行。
文章首先从银行业整体这个角度对商业银行1999-2008年全要素生产率的动态变化进行了分析,然后从组织形式、银行规模、以及银行个体三个层次对商业银行全要素生产率的动态变化进行了具体的分析,并对影响全要素生产率的一些因素进行了初步的判断,接着以前文测度的全要素生产率为因变量,以资产市场份额、资产规模、自有资本比率、银行配置、资产费用率、贷款质量、产权结构(虚拟变量)为自变量,构建Panel Data模型对影响中国商业银行全要素生产率的因素进行了定量地分析,最后根据上述的研究结果提出提升我国商业银行全要素生产率的途径。
本文的主要创新点可概括为：第一,方法上。本文以面板数据为基础,在运用基于DEA的Malmquist生产率指数模型对我国商业银行全要素生产率水平测算的基础上,构建了影响因素的Panel Data计量经济模型,对我国商业银行全要素生产率的影响因素进行了定量研究；第二,样本数据的选取上。本文将城市商业银行和外资银行也涵盖了进来,丰富了样本量,在时间上将考察期扩展到2008年,使样本数据得到扩展,能较全面的反映我国商业银行的全要素生产率情况。
This paper measures total factor productivity (TFP) of commercial banks in China from 1999-2008 by making use of Malmquist productivity index and DEA model based on bankscope database, then the Malmquist index is decomposed into two significant indexes (technological efficiency and technology change), and then uses panel data model to test the determinants of banks'efficiency in China. In this paper, I select the Four big state-owned banks, Joint banks, City commercial banks and Foreign banks as the sample banks.
Firstly, we analyze the dynamic change of commercial banks'TFP in China during 1999 to 2008 on the view of the whole banks system, then analyze the dynamic change and the reasons of commercial banks'TFP on three arrangement, involved commercial banks with different structure of property right, commercial banks with different scales, all single sample bank, and give some preliminary judges on the determinants of TFP. Second, we try to use panel data model to test the determinants of banks'total factor productivity in China and analyze the relation between TFP and banks'characters. We uses TFP as the independent variable, market share, capital scale, the ratio of equity to asset, the ratio of deposit to loan, the quality of loans, the structure of property right(dummy variable)as the dependent variable to construct Panel Data model to thoroughly analyze the determinants of commercial bank'TFP in China. At last, based on my study, I put forward the way to improve the TFP of commercial banking.
According to the accepted materials, there are two innovations in this paper. First, on the empirical method. We measures the TFP of China's commercial banks by using Malmquist productivity index and DEA model based on panel data, and then uses Panel Data model to analyze the determinants of banks'TFP in China. Second, on the selection of sample data. I select more banks than the existing studies to enrich the sample, as city commercial banks and foreign banks has been involved in, and the time period has been extended to 2008, in order to comprehensively reflect the general commercial bank of China.
引文
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